Vessel Activity-Global Fishing Watch

Overview

This dataset contains the Global Fishing Watch AIS-based fishing effort and vessel presence datasets .Data is based on fishing detections of >114,000 unique AIS devices on fishing vessels, of which ~70,000 are active each year. Fishing vessels are identified via a neural network classifier, vessel registry databases, and manual review by GFW and regional experts. Data are binned into grid cells 0.01 (or 0.1) degrees on a side and measured in units of hours. The time is calculated by assigning an amount of time to each AIS detection (which is the time to the previous position), and then summing all positions in each grid cell.

Fig 1.Map of Apparent Fishing Effort.

Data are available in two formats:

1. Fishing effort by flag state and gear type at 100th degree resolution (i.e. too large to upload)
2. Fishing effort by MMSI at 10th degree resolution (i.e. too large to upload)

Each version of the fishing effort dataset is accompanied by a table of vessel information (e.g. gear type, flag state, dimensions, etc. listed in the metadata folder).

Note: When attempting to download multiple files, you will receive an email containing download links after the data has been packaged. For large requests, it may take up to 24 hours to receive this email.

Versions

The fishing effort data is updated periodically to include more recent data and as improvements are made to our models and input data. Data from different major/minor versions should not be combined, as each version uses a different set of inputs.

Current version

2.0: Fishing effort data for 2012-2020 using the latest AIS algorithms, neural network models, and vessel registry database.

AIS Data

This MMSI number is usually unique to a single vessel and typically doesn’t change unless the vessel is red flagged. Most of the naval vessels investigated by Global Fishing Watch (GFW) did appear to use a particular MMSI number consistently even if the vessel only broadcast AIS occasionally. The MMSI numbers used by different naval vessels are listed on sites like Marine Traffic or even Wikipedia. But how can we really be sure that an MMSI number corresponds to a particular naval vessel? Naval vessels are frequently photographed, and it’s possible to get a sequence of port visits based on photos uploaded to sites like warshipcam.com. This documented series of port visits can then be compared to the AIS track to confirm that an MMSI corresponds to a particular vessel.(Clavelle,Tyler Global Fishing Watch)

Data Wrangling & Exploration

In a snapshot, some of the steps for data exploration in this analysis include :

• Import data (i.e. Data imported here was downloaded from GFW website/ Although in a separate R scripts in model_results folder I used BigQuery). The dataset used below is fishing-vessels-v2.csv saved as vessels.Rda
• Clean data (I mainly worked with vessels.Rda. The other files were too large and exceeded Github 2 GB limit.)
• Format data properly
• Create new variables
• Get an overview about the complete data (i.e. especially missing data)
• Split data into training and test set using stratified sampling (i.e. strata is geartype here)
• Discover and visualize the data to gain insights

knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)

Loading packages

knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)


# ### Geo Packages library(raster) # for working with rasters needs to run
# before tidyverse or select() wont work library(maps) # additional helpful
# mapping packages library(maptools) library(tmap) library(rgeos)
# library(gstat) library(sf) # for vector data
# #install.packages('rnaturalearth') library(rnaturalearth) library(sp)

### Data Cleaning & wrangling
library(tidyverse)  # for general data wrangling and plotting
library(purrr)
library(lubridate)  # for working with dates
library(here)  # for paths
library(janitor)  # for cleaning var names 
library(formatR)  #used for chunk tidy code formatting
library(knitr)

### ML packages
library(tidymodels)
library(glmnet)  # Lasso and Elastic-Net Regularized Generalized Linear Models 
library(vip)
library(randomForest)
library(ranger)  #for random forest
library(xgboost)
library(kableExtra)
library(readr)
library(corrr)
library(corrplot)
library(klaR)
library(tune)

### Visualization
library(corrplot)  # to create a correlation matrix
library(rpart.plot)  # for trees to plot an rpart model
# library(fishualize) # library(fishmethods)
library(GGally)
library(visdat)
library(skimr)
library(kableExtra)

### Query library(bigrquery) #Interface to Google's 'BigQuery' 'API'

Import Data

Daily fishing effort data are available for download in the following two formats:

• Daily Fishing Effort and Vessel Presence at 100th Degree Resolution by Flag State and GearType, 2012-2020
• Daily Fishing Effort and at 10th Degree Resolution by MMSI, 2012-2020

Upon downloading each dataset, users will receive a .zip file. When using GFW website you could select how many files you want to download. Some of the files downloaded for this analysis include data readme.txt files (i.e metadata folder), daily_csvs folder (i.e MMSI, fleet) and daily fishing effort and vessel presencefishing-vessels-v2.csv saved as
vessels.Rda.This data can be used in conjunction with the “Daily Fishing Effort and at 10th Degree Resolution by MMSI” data to explore vessel and fleet characteristics and provide details (gear type, flag state, etc.) for the MMSI being analyzed.

The daily csvs are named according to the YYYY-MM-DD.csv format. The files generally increase in size over time due to the higher number of satellites receiving AIS data.

# load("world.Rda")

# load("eezs.Rda")

Loading the data

# load vessels yearly data
load("vessels.Rda")
# head(vessels)

Geartypes:

The current version of the GFW vessel classification neural net classifies fishing vessels into sixteen categories.

Fig 2.Map of Apparent Fishing Effort.

Longline fishermen use lines that can extend for up to 50 miles, with thousands of baited hooks branching off from the main line. Unfortunately, the baited hooks attract a vast array of species that are not intentionally targeted, including diving birds, turtles, etc. If an animal becomes hooked, it is often seriously injured or dead by the time the gear is retrieved. Using more selective gear instead of longlines is proven to reduce bycatch and improve fishing efficiency.(oceana.org)

Some classes of the outcome variable chosen for this analysis are described below :

• fishing: a combination of vessels of unknown fishing gear
• drifting_longlines: drifting longlines
• seiners: vessels using seine nets, including potential purse seine vessels targeting tuna and other species, as well as danish and other seines
• purse_seines: purse seines, both pelagic and demersal
• tuna_purse_seines: large purse seines primarily fishing for tuna.
• other_purse_seines: purse seiners fishing for mackerel, anchovies, etc, often smaller and operating nearer the coast than tuna purse seines.
• other_seines: danish seines and other seiners not using purse seines.
• trawlers: trawlers, all types
• pole_and_line: vessel from which people fish with pole and line.
• trollers: vessel that tows multiple fishing lines.
• fixed_gear: a category that includes potential set longlines, set gillnets, and pots and traps

Exploratory Data Analysis

# To get a first impression of the data we take a look at the top 4 rows:
# install.packages('gt')
library(gt)

# vessels data quick exploration
vessels %>%
    slice_head(n = 4) %>%
    gt()  # print output using gt

# Creating and storing a table of yearly vessels geartype
vessels_gear <- vessels %>%
    group_by(vessel_class_gfw) %>%
    summarize(sum_class = n()) %>%
    arrange(desc(sum_class))

# Proportion of each vessel's geartype
vessels %>%
    count(vessel_class_gfw) %>%
    mutate(prop = n/sum(n)) %>%
    arrange(-(round(prop, 2))) %>%
    kable(caption = "Fishing Gear Class") %>%
    column_spec(1:3, width = "10em") %>%
    kable_styling(bootstrap_options = "striped")

# howw many geartypes in the dataset
vessels %>%
    ggplot(aes(x = reorder(vessel_class_gfw, vessel_class_gfw, function(x) length(x)))) +
    geom_bar(aes(fill = vessel_class_gfw, stat = "identity")) + theme_bw() + labs(x = "") +
    theme(axis.ticks.y = element_line(size = 1), panel.grid = element_blank(), legend.key = NULL) +
    coord_flip()

• There are sixteen levels of the outcome, or primary classes of geartype, in the data. The classes with the smallest numbers of geartype are seiners,trollers, pure seine and other seines.

• We’ll handle the smaller classes by simply filtering them out. Filter the entire data set to contain only fleets whose geartype is set_longlines, set_gillnets, squid_jigger, tuna_purse_seines, fixed_gear, pole_and_line.

# Creating a vessels df and filtering for the geartype of choice

vessels_df <- vessels %>%
    filter(vessel_class_gfw %in% c("set_longlines", "set_gillnets", "tuna_purse_seines",
        "fishing", "other_purse_seines", "\tdrifting_longlines")) %>%
    mutate(flag = factor(flag_gfw), vessel_class_gfw = factor(vessel_class_gfw))

# Creating a subset of the vessel_df
vessels_sub <- sample_n(vessels_df, 10000) %>%
    imputeTS::na.replace(0)

# Let's look at the proportion of geartype within our subset
vessels_sub %>%
    count(vessel_class_gfw) %>%
    mutate(prop = n/sum(n)) %>%
    arrange(-(round(prop, 2))) %>%
    kable(caption = "Fishing Gear Class") %>%
    column_spec(1:3, width = "10em") %>%
    kable_styling(bootstrap_options = "striped")

# Na's is.na(vessels_sub) %>% colSums()
vis_dat(vessels_sub)

# Visualization the classes of our our outcome variable
vessels_sub %>%
    ggplot(aes(x = reorder(vessel_class_gfw, vessel_class_gfw, function(x) length(x)))) +
    geom_bar(aes(fill = vessel_class_gfw, stat = "identity")) + theme_bw() + labs(x = "") +
    theme(axis.ticks.y = element_line(size = 1), panel.grid = element_blank(), legend.key = NULL) +
    coord_flip()

Machine Learning Models

Principal components analysis (PCA)

Principal components analysis is an ordination method allowing us to glean as much about our multivariate data as possible in a simplified number of dimensions. Here, we’ll use our dataset of fishing vessels to explore variable relationships and clustering by geartype in a PCA biplot. For this example with PCA, we will only use the structure size measurements (bill length and depth, mass, and flipper length).

We use the R built-in function prcomp() to calculate the principal components of the dataset. It’s important to specify scale = TRUE so that each of the variables in the dataset are scaled to have a mean of 0 and a standard deviation of 1 before calculating the principal components.

Also note that eigenvectors in R point in the negative direction by default, so we’ll multiply by -1 to reverse the signs.

# install.packages('imputeTS')

library(imputeTS)  # for na.replace
# calculate principal components
vessels_pca <- vessels_sub %>%
    select_if(is.numeric) %>%
    imputeTS::na.replace(0) %>%
    scale() %>%
    prcomp()



# reverse the signs
vessels_pca$rotation <- -1 * vessels_pca$rotation

# display principal components
vessels_pca$rotation

# reverse the signs of the scores
vessels_pca$x <- -1 * vessels_pca$x

# display the first six scores head(vessels_pca$x)

# Variance explained by each PC
screeplot(vessels_pca, type = "lines")

screeplot(vessels_pca, type = "barplot")

library(pca3d)  # 2d & 3d pca plots

pca2d(vessels_pca, components = 1:2, col = NULL, title = NULL, new = FALSE, axes.color = "black",
    bg = "white", radius = 1, shape = 18, palette = palette(hcl.colors(2, "viridis")))

pca_3d <- pca3d(vessels_pca, components = 1:3)

## a bit more fancy: black background, white axes, centroids
pca2_3d <- pca3d(vessels_pca, fancy = TRUE, bg = "black", axes.color = "white", new = TRUE)

The PCA analysis indicates that PC1 & PC2 represents a good amount of the data compare to the rest.

PCA (CPC1, PC2, PC3)

PCA (CPC1, PC2, PC3)

Data splitting

set.seed(3435)

# Split the dataset into train and test

vessels_split <- initial_split(vessels_sub, prop = 0.8, strata = vessel_class_gfw)
vessels_train <- training(vessels_split)
vessels_test <- testing(vessels_split)
# dim(vessels_train)

In order to have a good training set, we set up prop equal to 80% and our training set has 29 variables with 7998 obs.

Training set Visualisation

1. Corrplot

vessels_train %>%
    select_if(is.numeric) %>%
    dplyr::select(-vessel_class_inferred_score) %>%
    dplyr::select(-starts_with("fishing")) %>%
    cor(use = "complete.obs") %>%
    corrplot(type = "lower", diag = FALSE)

From the corrplot, we can see that tonnage_gt_ registery and engine_power_kw_gfw are positively correlated but length_mregitery is negatively correlated with engine power.

2. Barplot

vessels_train %>%
    ggplot(aes(x = vessel_class_gfw)) + geom_bar(aes(fill = vessel_class_gfw))

From the barplot of the chosen predictors : “set_longlines”, “set_gillnets”, “tuna_purse_seines”, “fishing”,
“other_purse_seines”,” drifting_longlines”. We notice that fishng class has more observations than the others.

Cross Validation folds

For the cross-validation, I’m using v = 3 folds and stratifying the outcome variable for geartype vessel_class_gfw.

set.seed(3435)
vessels_folds <- vfold_cv(data = vessels_train, v = 3, strata = vessel_class_gfw)

Using a subset of the data and given that the training set only has 29 vars and many observations. we chose to use a lower v-folds to avoid having an imbalance in a given class where an outcome variable may either be present or not.

Vessel recipe

In this section, we chose our model recipe : predictors for the outcome variable. Note that I used na.replace(0) for the fishing hours yearly variables due to to the large numbers of missing data.

vessels_recipe <- recipe(vessel_class_gfw ~ length_m_gfw + mmsi + tonnage_gt_gfw +
    fishing_hours_2012 + fishing_hours_2015 + fishing_hours_2018 + fishing_hours_2013 +
    fishing_hours_2016 + fishing_hours_2019 + fishing_hours_2014 + fishing_hours_2017 +
    fishing_hours_2020, data = vessels_train) %>%
    step_rollimpute(all_predictors()) %>%
    step_naomit(everything(), skip = TRUE) %>%
    step_dummy(all_nominal_predictors()) %>%
    step_normalize(all_predictors()) %>%
    step_novel(all_nominal(), -all_outcomes()) %>%
    step_zv(all_numeric(), -all_outcomes()) %>%
    step_corr(all_predictors(), threshold = 0.7, method = "spearman")



# glimpse(prepped_data)

# Elastic Net Model
elastic_net_spec <- multinom_reg(penalty = tune(), mixture = tune()) %>%
    set_mode("classification") %>%
    set_engine("glmnet")

en_workflow <- workflow() %>%
    add_recipe(vessels_recipe) %>%
    add_model(elastic_net_spec)

en_grid <- grid_regular(penalty(range = c(-5, 5)), mixture(range = c(0, 1)), levels = 10)

library(tictoc)
tic()
tune_res_1 <- tune_grid(
  en_workflow,
  resamples = vessels_folds, 
  grid = en_grid
)

# save(tune_res_1, file ="tune_res.Rda")
# load("tune_res.Rda")
ggplot2::autoplot(tune_res_1)

toc()

## 35.958 sec elapsed

tune_res_1 <- tune_grid(
  en_workflow,
  resamples = vessels_folds, 
  grid = en_grid
)

autoplot(tune_res_1)

best_model <- select_best(tune_res_1, metric = "roc_auc")

en_final <- finalize_workflow(en_workflow, best_model)

en_final_fit <- fit(en_final, data = vessels_train)

predicted_data <- augment(en_final_fit, new_data = vessels_test) %>% 
  dplyr::select(vessel_class_gfw, starts_with(".pred")) 


predicted_data %>% 
  conf_mat(truth =vessel_class_gfw, estimate = .pred_class) %>%
  autoplot(type = "heatmap")

predicted_data %>% roc_auc(vessel_class_gfw, .pred_fishing:.pred_tuna_purse_seines)

## # A tibble: 1 × 3
##   .metric .estimator .estimate
##   <chr>   <chr>          <dbl>
## 1 roc_auc hand_till      0.755

tree_spec <- decision_tree() %>%
  set_engine("rpart")

class_tree_spec <- tree_spec %>%
  set_mode("classification")



class_tree_wf <- workflow() %>%
  add_recipe(vessels_recipe) %>%  
  add_model(class_tree_spec %>% set_args(cost_complexity = tune())) 

param_grid_tr <- grid_regular(cost_complexity(range = c(-3, -1)), levels = 10)



tune_res_tr <- tune_grid(
  class_tree_wf, 
  resamples = vessels_folds, 
  grid = param_grid_tr, 
  metrics = metric_set(roc_auc)
)


autoplot(tune_res_tr)

tree_metrics <- collect_metrics(tune_res_tr) %>% 
arrange(desc((mean)))

best_complexity <- select_best(tune_res_tr )

class_tree_final <- finalize_workflow(class_tree_wf, best_complexity)

class_tree_final_fit <- fit(class_tree_final, data = vessels_train)

class_tree_final_fit %>%
  extract_fit_engine() %>%
  rpart.plot()

class_rf_model = rand_forest(mtry = tune(), trees = tune(), min_n = tune()) %>% 
  set_engine("ranger", importance = "impurity") %>% 
  set_mode("classification") 


class_rf_wflw = workflow() %>% 
  add_recipe(vessels_recipe) %>% 
  add_model(class_rf_model) 


library(tictoc)
tic()

rf_grid <- grid_regular(
  mtry(range = c(1, 8)), #set mtry to 1-8
  trees(range = c(100,200)), #set trees 100 to 200
  min_n(range = c(2, 10)), # set min_n between 2 and 20
  levels = 8) #what's a good level?

rf_tune <- tune_grid( 
  class_rf_wflw,
  resamples = vessels_folds, 
  grid = rf_grid, 
  metrics = metric_set(roc_auc) 
  )
toc()

## 7608.787 sec elapsed

autoplot(rf_tune)

saveRDS(rf_tune,file = "rf_tune.RDS")
rf_tune_results <- readRDS("rf_tune.RDS")

rf_tree_metrics <- collect_metrics(rf_tune) %>% 
arrange(desc((mean)))
rf_tree_metrics

## # A tibble: 512 × 9
##     mtry trees min_n .metric .estimator  mean     n std_err .config             
##    <int> <int> <int> <chr>   <chr>      <dbl> <int>   <dbl> <chr>               
##  1     6   185    10 roc_auc hand_till  0.818     3 0.00472 Preprocessor1_Model…
##  2     6   200    10 roc_auc hand_till  0.818     3 0.00326 Preprocessor1_Model…
##  3     6   185     8 roc_auc hand_till  0.818     3 0.00395 Preprocessor1_Model…
##  4     7   185    10 roc_auc hand_till  0.817     3 0.00377 Preprocessor1_Model…
##  5     5   100    10 roc_auc hand_till  0.817     3 0.00246 Preprocessor1_Model…
##  6     5   171     8 roc_auc hand_till  0.817     3 0.00432 Preprocessor1_Model…
##  7     5   142     8 roc_auc hand_till  0.817     3 0.00217 Preprocessor1_Model…
##  8     6   200     7 roc_auc hand_till  0.817     3 0.00239 Preprocessor1_Model…
##  9     6   142    10 roc_auc hand_till  0.817     3 0.00263 Preprocessor1_Model…
## 10     5   185    10 roc_auc hand_till  0.817     3 0.00234 Preprocessor1_Model…
## # … with 502 more rows

best_complexity_rf <- select_best(rf_tune)
best_complexity_rf

## # A tibble: 1 × 4
##    mtry trees min_n .config               
##   <int> <int> <int> <chr>                 
## 1     6   185    10 Preprocessor1_Model502

rf_final <- finalize_workflow(class_rf_wflw, select_best(rf_tune)) 

rf_fit <- fit(rf_final, vessels_train)  

 vip_rf <- rf_fit %>% 
  extract_fit_engine() %>%
  vip()
 
 vip_rf

boost_model = boost_tree(trees = tune()) %>% #set up boosted tree model
  set_engine("xgboost") %>% # xgboost engine calculates lambda #high lambda is good 
  set_mode("classification") 


boost_wflw = workflow() %>%
  add_recipe(vessels_recipe) %>% 
  add_model(boost_model) 

boost_grid <- grid_regular(
  trees(range = c(10,2000)), 
  levels = 10) 

boost_tune_res <- tune_grid( 
  boost_wflw, 
  resamples = vessels_folds, 
  grid = boost_grid, 
  metrics = metric_set(roc_auc) 
  )

# saveRDS(boost_tune_res,file = "boost_tune.RDS")
# boost_results <- readRDS("boost_tune.RDS")
# autoplot(boost_results)

autoplot(boost_tune_res) 

boost_metrics <- collect_metrics(boost_tune_res) %>% 
arrange(desc((mean)))

best_complexity_boost <- select_best(boost_tune_res)

models = full_join(tree_metrics, rf_tree_metrics)
joined_models = full_join(models, boost_metrics)
joined_models 

## # A tibble: 532 × 10
##    cost_complexity .metric .estimator  mean     n std_err .config     mtry trees
##              <dbl> <chr>   <chr>      <dbl> <int>   <dbl> <chr>      <int> <int>
##  1         0.001   roc_auc hand_till  0.768     3 0.00399 Preproces…    NA    NA
##  2         0.00167 roc_auc hand_till  0.763     3 0.00416 Preproces…    NA    NA
##  3         0.00278 roc_auc hand_till  0.741     3 0.0106  Preproces…    NA    NA
##  4         0.00464 roc_auc hand_till  0.728     3 0.00504 Preproces…    NA    NA
##  5         0.00774 roc_auc hand_till  0.717     3 0.00240 Preproces…    NA    NA
##  6         0.0129  roc_auc hand_till  0.716     3 0.00177 Preproces…    NA    NA
##  7         0.0215  roc_auc hand_till  0.643     3 0.0166  Preproces…    NA    NA
##  8         0.0359  roc_auc hand_till  0.603     3 0.00613 Preproces…    NA    NA
##  9         0.0599  roc_auc hand_till  0.603     3 0.00613 Preproces…    NA    NA
## 10         0.1     roc_auc hand_till  0.603     3 0.00613 Preproces…    NA    NA
## # … with 522 more rows, and 1 more variable: min_n <int>

rf_test_fit = augment(rf_fit, new_data = vessels_test) 
roc_auc(rf_test_fit, truth = vessel_class_gfw, .pred_fishing:.pred_tuna_purse_seines) 

## # A tibble: 1 × 3
##   .metric .estimator .estimate
##   <chr>   <chr>          <dbl>
## 1 roc_auc hand_till      0.828

autoplot(roc_curve(rf_test_fit, truth = vessel_class_gfw, .pred_fishing:.pred_tuna_purse_seines)) 

conf_mat(rf_test_fit, truth = vessel_class_gfw, estimate = .pred_class) %>% 
  autoplot(type = "heatmap") + 
  theme(axis.text.x = element_text(angle = 90, hjust=1)) 

library(kknn)
knn_spec <- 
  nearest_neighbor(neighbors = 4) %>% # we can adjust the number of neighbors 
  set_engine("kknn") %>% 
  set_mode("classification") 

knn_wflow <-
 workflow() %>%
 add_recipe(vessels_recipe) %>% 
 add_model(knn_spec)

knn_res <- 
  knn_wflow %>% 
  fit_resamples(
    resamples = vessels_folds, 
    metrics = metric_set(
      recall, precision, f_meas, 
      accuracy, kap,
      roc_auc, sens, spec),
    control = control_resamples(save_pred = TRUE)
    ) 

knn_res %>% collect_metrics(summarize = TRUE)

## # A tibble: 8 × 6
##   .metric   .estimator  mean     n  std_err .config             
##   <chr>     <chr>      <dbl> <int>    <dbl> <chr>               
## 1 accuracy  multiclass 0.430     3 0.00145  Preprocessor1_Model1
## 2 f_meas    macro      0.414     3 0.00376  Preprocessor1_Model1
## 3 kap       multiclass 0.207     3 0.00114  Preprocessor1_Model1
## 4 precision macro      0.411     3 0.00526  Preprocessor1_Model1
## 5 recall    macro      0.417     3 0.00227  Preprocessor1_Model1
## 6 roc_auc   hand_till  0.716     3 0.000827 Preprocessor1_Model1
## 7 sens      macro      0.417     3 0.00227  Preprocessor1_Model1
## 8 spec      macro      0.841     3 0.000209 Preprocessor1_Model1

# knn_results <- saveRDS(boost_tune_res,file = "boost_tune.rds")
# knn_results <- readRDS("boost_results.rds")

#  # ?keras::install_keras()
# # install_keras(Tensorflow = "1.13.1",
# # restart_session = FALSE)
# library(keras)
# library(tensorflow)
# 
# nnet_spec <-
#   mlp() %>%
#   set_mode("classification") %>% 
#   set_engine("keras", verbose = 0) 
# 
# nnet_wflow <-
#  workflow() %>%
#  add_recipe(vessels_recipe) %>% 
#  add_model(nnet_spec)
# 
# nnet_res <- 
#   nnet_wflow %>% 
#   fit_resamples(
#     resamples = vessels_folds, 
#     metrics = metric_set(
#       recall, precision, f_meas, 
#       accuracy, kap,
#       roc_auc, sens, spec),
#     control = control_resamples(save_pred = TRUE)
#     ) 

Conclusion

The best performing models out of those tested are elestic tuning and neural network. The model would do much better if coupled with the geographic data for longitude and latitude for better estimates of vessels geartype used. This type of modeling can help in the future with marine conservation and help improve traditional fishing methods for more sustainable ones.

References

“Copyright [2022], Global Fishing Watch, Inc., www.globalfishingwatch.org.”

“Global Fishing Watch has made every attempt to ensure the completeness, accuracy and reliability of the information provided on this Site. However, due to the nature and inherent limitations in source materials for information provided, Global Fishing Watch qualifies all designations of vessel fishing activity, including synonyms of the term”fishing activity,” such as “fishing” or “fishing effort,” as “apparent,” rather than certain. And accordingly, the information is provided “as is” without warranty of any kind.”

Clavelle, Tyler. “Working with Our Downloadable Public Data in R.” Global Fishing Watch, 29 May 2019, https://globalfishingwatch.org/data/working-with-our-downloadable-public-data-in-r/.

Flanders Marine Institute (2019). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11. Available online at https://www.marineregions.org/. https://doi.org/10.14284/386

“Harmful Gear: Trawls, Longlines, & Gillnets.” Oceana USA, https://usa.oceana.org/harmful-gear-trawls-longlines-gillnets/.